Segmentation is the partitioning of an image into meaningful regions which correspond to (parts of) the objects to be seen. The better your initial segmentation the greater your chance to extract correct semantic information from the image. Among other topics, I've written papers on how to integrate edge and junction detection in order to get correct boundary topology, how to obtain and represent topological partitionings optimally in a computer, and on how sampling affects the correct representation of geometric shapes.

Reusable Software for Computer Vision:

Software reuse has been a goal for a long time, but does not work as well as it should. I've written some design patterns which catalog some of the basic ideas of reusable architectures. At the level of algorithms, these ideas are best realized by generic programming (the programming style of the C++ Standard Library). Therefore, I developed a novel computer vision library VIGRA which is based on generic programming. A number of papers and, most comprehensively, my PhD thesis describe the ideas of this library and report our experiences with reusable algorithms.

While I was at Fraunhofer-Institute for Computer Graphics in Rostock, I helped developing a system which allows to extract 3-dimensional object models from image sequences. I.e., to develop a virtual reality simulation containing natural objects (such as buildings), one takes some photos of each object and uses our system to generate realistic geometric models (including textures). Here are the official descriptions of the projects REKO and ERSO. A description of the ERSO project and the underlying concepts can be found in this paper.